In this post, we will take a look at how we can use Google Cloud Vision from a Spring Boot application. With Google Cloud Vision it is possible to derive all kinds of things from images, like labels, face and text recognition, etc. As a bonus, some examples with Python are provided too.
A couple of weeks ago, my team leader dropped a book on my desk. “Read it.”, he said. So I started reading The Phoenix Project: A Novel about It, Devops, and Helping Your Business Win written by Gene Kim, Kevin Behr and George Spafford. Now that I have finished my first reading of the book, I wanted to share some thoughts about it.
In this post we will explore how we can use Google Cloud Platform’s (GCP) Pub/Sub service in combination with a Spring Boot application using Spring Integration. We will send a message to a sender application which publishes the message to a Topic where a receiver application receives the messages of a Subscription.
You are looking for an easy way to automatically build your application in the Cloud? Then maybe Google Cloud Platform (GCP) Cloud Build is something for you. In this post, we will build a Spring Boot Maven project with Cloud Build, create a Docker image for it and push it to GCP Container Registry.
In this post, we will take a look at how we can use Google Cloud Platform (GCP) SQL as a database for our Spring Boot application. We will investigate how we can use the Cloud database from our development machine and how we can use it from GCP itself.
In this post we are going to deploy a Spring Boot application to the Google Cloud Platform (GCP) App Engine. First, we will take a look at the differences between the standard and flexible environment of App Engine. After that, we will describe step by step how the deployment to GCP App Engine can be accomplished.
The past year, we wrote some articles using Minikube as Kubernetes cluster in order to experiment with. In this post, we will take our first steps into Google Cloud Platform (GCP) and more specifically of Kubernetes Engine. Let’s see whether going to the Cloud makes our lives even easier ;-). We will create a GCP account, create a Kubernetes cluster, deploy our application manually and deploy by means of Helm.
When you pull a Docker image, you will notice that it is pulled as different layers. Also, when you create your own Docker image, several layers are created. In this post we will try to get a better understanding of Docker layers.
In a previous post, we talked about how we can check our Docker images for any known vulnerabilities by means of Anchore Engine. This still required a manual action. Wouldn’t it be great if we could incorporate Anchore Engine into our Jenkins CI build job or pipeline? In this post, we will take a look at how we can accomplish this by means of the Anchore Container Image Scanner Jenkins Plugin.
When using Docker containers in production, we need to ensure that we are following best practices. In this post, we will focus on Ensure images are scanned and rebuilt to include security patches from the CIS Docker Community Benchmark which we discussed previously. The item states that you should scan your images “frequently” for any vulnerabilities and then take the necessary actions to mitigate these vulnerabilities. We will use Anchore Engine in order to accomplish this.